Hey!! This article featured the NPTEL Data Science for Engineers Week 7 Assignment Answers. The answers for the Week 6 Data Science for Engineers are already provided, visit the link below. The answers provided here are 100% Correct.

Also Read: NPTEL Data Science for Engineers Week 6 Assignment 2025 Answers

For Faster Updates and upcoming assignments,  join with us 
Join us on Telegram 👉 CLICK HERE 

NPTEL Data Science for Engineers Week 7 Assignment 2025

In this Week 7, the following topics are discussed:

• Cross Validation
• Multiple Linear Regression
• Logistic Regression
• Dataset

Last Date: 12-07-2025

Q1. Which among the following is not a type of cross-validation technique?

A. LOOCV
B. k-fold cross validation
C. Validation set approach
D. Bias variance trade off

Answer: [ D ] Bias variance trade off

Q2. Which among the following is a classification problem?

A. Predicting the average rainfall in a given month.
B. Predicting whether a patient is diagnosed with a disease or not.
C. Predicting the price of a house.
D. Predicting whether it will rain or not tomorrow.

Answer: [ B ] [ D ]  
               Predicting whether a patient is diagnosed with a disease or not.
               Predicting whether it will rain or not tomorrow.

Common data for Q3 - Q4

Consider the following confusion matrix for the classication of Hatchback and SUV:

Q3. Find the accuracy of the model.

A. 0.95
B. 0.55
C. 0.45
D. 0.88

Answer: [ A ] 0.95

Q4. Find the sensitivity of the model.

A. 0.95
B. 0.55
C. 1
D. 0.88

Answer: [ C ] 1

Q5. Under the ‘family’ parameter of glm() function, which one of the following distributions correspond to logistic regression for a variable with binary output?

A. Binomial
B. Gaussian
C. Gamma
D. Poisson

Answer: [ A ] Binomial

Use the following information to answer Q6, Q7, Q8, Q9, and Q10:

Load the dataset iris.csv (add the link sent in the email) as a dataframe irisdata, with the first column as index headers, first row as column headers, dependent variable as factor variable, and answer the following questions. The iris dataset contains four Sepal and Petal features (Sepal Length, Sepal Width, Petal Length, Petal Width, all in cm) of 50 equal samples of 3 different species of the iris flower (Setosa, Versicolor, and Virginica).

Q6. What is the dimension of the dataframe?

A. (150, 5)
B. (150, 4)
C. (50, 5)
D. None of the above

Answer: [ A ] (150, 5)

Q7. What can you comment on the distribution of the independent variables in the dataframe?

A. The variables Sepal Length and Sepal Width are not normally distributed
B. All the variables are normally distributed
C. The variable Petal Length alone is normally distributed
D. None of the above

Answer: [ B ] All the variables are normally distributed

Q8. How many rows in the dataset contain missing values?

A. 10
B. 5
C. 25
D. 0

Answer: [ D ] 0

Q9. Which of the following code blocks can be used to summarize the data (finding the mean of the columns PetalLength and PetalWidth), similar to the one given below.

A. lapply(irisdata[, 3:4], mean)
B. sapply(irisdata[, 3:4], 2, mean)
C. apply(irisdata[, 3:4], 2, mean)
D. apply(irisdata[, 3:4], 1, mean)

Answer: [ A ] [ C ] 
               lapply(irisdata[, 3:4], mean)
               apply(irisdata[, 3:4], 2, mean)

Q10. What can be interpreted from the plot shown below?

A. Sepal widths of Versicolor flowers are lesser than 3 cm.
B. Sepal lengths of Setosa flowers are lesser than 6 cm.
C. Sepal lengths of Virginica flowers are greater than 6 cm.
D. Sepals of Setosa flowers are relatively more wider than Versicolor flowers.

Answer: [ B ] [ D ] 
               Sepal lengths of Setosa flowers are lesser than 6 cm.
               Sepals of Setosa flowers are relatively more wider than Versicolor flowers.

Conclusion

Join us on Telegram 👉 CLICK HERE